Signal and pattern detection or classification by estimation of continuous dynamical models
Download PDFInfo
 Publication number
 US6564176B2 US6564176B2 US09932450 US93245001A US6564176B2 US 6564176 B2 US6564176 B2 US 6564176B2 US 09932450 US09932450 US 09932450 US 93245001 A US93245001 A US 93245001A US 6564176 B2 US6564176 B2 US 6564176B2
 Authority
 US
 Grant status
 Grant
 Patent type
 Prior art keywords
 data
 signal
 signals
 processing
 derivative
 Prior art date
 Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
 Active
Links
Images
Classifications

 G—PHYSICS
 G06—COMPUTING; CALCULATING; COUNTING
 G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
 G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
 G06K9/62—Methods or arrangements for recognition using electronic means
 G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
Abstract
Description
This application is a continuation of U.S. application Ser. No. 09/105,529, filed Jun. 26, 1998, now U.S. Pat. No. 6,278,961, which claims the benefit of U.S. Provisional Application No. 60/051,579, filed Jul. 2, 1997.
The U.S. Government has a paidup license in this invention and the right in limited circumstances to require the patent owner to license others on reasonable terms as provided for by the terms of contract No. N0042197C1048 awarded by the United States Navy.
1. Field of Invention
This invention relates to signal processing and pattern recognition, specifically to a new way of characterization of data as being generated by dynamical systems evolving in time and space.
2. Discussion of Prior Art
Our invention is based on novel ideas in signal processing derived by us from the theory of dynamical systems. This field is relatively new, and we specifically have developed our own theoretical framework which makes our approach unique. While we do not include the full theory here, it gives our invention a solid analytical foundation.
The theory of dynamicallybased detection and classification is still under active the oretical development. The main idea of our approach is to classify signals according to their dynamics of evolution instead of particular data realizations (signal measurements). Our method opens the possibility of a very compact and robust classification of signals of deterministic origin.
Modeling of dynamical systems by ordinary differential equations and discrete maps reconstructed from data has been proposed by several researchers, and their results have been published in open scientific journals (for example, J. P. Crutchfield and B. S. McNamara, Complex Systems 1(3), p.417 [8]). Modeling can generally be performed on lownoise data when very accurate dynamical models can be found to fit the data. This may be considered a prior art, though in the current invention we do not use parametric dynamical systems to model data, rather we use them for detection and classificationof signals. Correspondingly, in the high noise case, our model equations need not necessarily be exact, since we do not try to use the estimated equations to predict the data. This makes an important difference between modeling approaches proposed in the prior art and our detection/classification framework: while model selection is subject to numerous restrictions, our algorithmic chain can always be implemented, regardless of the source of the signal. Currently, no practical devices or patents exist using this technology.
Note: in all references throughout this document, we use the term “signals” to mean the more general category of “time series, signals, or images”.
Accordingly, several objects and advantages of our invention are:
1. to provide a theoretically wellfounded method of signal processing and time series analysis which can be used in a variety of applications (such as Sonar, Radar, Lidar, seismic, acoustic, electromagnetic and optic data analysis) where deterministic signals are desired to be detected and classified;
2. to provide possibilities for both softwarebased and hardwarebased implementations;
3. to provide compatibility of our device with conventional statistical and spectral processing means bestsuited for a particular application;
4. to provide amplitude independent detection and classification for stationary, quasistationary and nonstationary (transient) signals;
5. to provide detection and classification of signals where conventional techniques based on amplitude, powerspectrum, covariance and linear regression analyses perform poorly;
6. to provide recognition of physical systems represented by scalar observables as well as multivariate measurements, even if the signals were nonlinearly transformed and distorted during propagation from a generator to a detector;
7. to provide multidimensional feature distributions in a correspondingly multidimensional classification space, where each component (dimension) corresponds to certain linear or nonlinear signal characteristics, and all components together characterize the underlying state space topology for a dynamical representation of a signal class under consideration;
8. to provide robust decision criteria for a wide range of parameters and signals strongly corrupted by noise;
9. to provide realtime processing capabilities where our invention can be used as a part of field equipment, with onboard or remote detectors operating in evolving environments;
10. to provide operational user environments both under human control and as a part of semiautomated and fullyautonomous devices;
11. to provide methods for the design of dynamical filters and classifiers optimized to a particular category of signals of interest;
12. to provide a variety of different algorithmic implementations, which can be used separately or be combined depending on the type of application and expected signal characteristics;
13. to provide learning rules, whereby our device can be used to build and modify a database of features, which can be subsequently utilized to classify signals based on previously processed patterns;
14. to provide compression of original data to a set of model parameters (features), while retaining essential information on the topological structure of the signal of interest; in our typical parameter regimes this can provide enormous compression ratios on the order of 100:1 or better.
Further objects and advantages of our invention will become apparent from a consideration of the flowcharts and the ensuing description.
The objects and advantages of the invention will be understood by reading the following detailed description in conjunction with the drawings in which:
FIG. 1 is a block diagram of the principal algorithm for signal and pattern detection and classification by estimation continuous dynamical models. Each block is given several implementations and algorithmic details are explained in the corresponding text of the Description.
FIG. 2 is ablock diagram of the first embodiment. The difference between this embodiment and the general processing chain shown in FIG. 1 is that a preferable implementation is indicated for each step in the processing chain. Detection/classification decisions are made upon postprocessing of feature distributions.
The theory of Detection/Classification by Estimation of Continuous Dynamical Models is under active development by us. There are strong results clearly indicating expected theoretical performance on simulated and realworld data sets from the detectors/classifiers built as embodiments of our invention.
Though the general processing chain described by FIG. 1 and FIG. 2 gives the full disclosure of our invention, we must stress here that several components can be implemented in a variety of ways. By building different embodiments of our invention, one can design software and hardware based devices which are best suited for a particular application.
The following are examples of implementations for each corresponding component in the processing scheme:
Data acquisition (FIG. 1, block 101). Can be performed by means of
A1 data digitized while recording from single or multiple sensors, including: acoustic, optical, electromagnetic, seismic sensors but not restricted to this set;
A2 data retrieved from a storage device such as optical or magnetic disks, tapes and other types of permanent or reusable memories;
A3 data generated and/or piped by another algorithm, code, driver, controller, signal processing board and so on;
As a result of this step we assume that either a set of digitized scalar or vector data is obtained and this data set may contain information to be detected, classified, recognized, modeled, or to be used as a learning set. The data consists of, or is transformed to, a set of ordered numbers x_{i}, where the index i=1, 2 . . . , L can indicate time, position in space, or any other independent variable along which data evolution occurs. We will also refer to x_{i }as a “signalofinterest” (or simply, a signal), “observations” or “measurements”.
Data preprocessing (FIG. 1, block 102). Can be performed by means of
B1 normalizing the data;
B2 filtering the data;
B3 smoothing the data;
B4 continuously transforming the data.
It is convenient, but not necessary, to organize data in a D×L_{eff }data matrix X, where the rows are Ddimensional observations (an independent variable is indexed from 1 to L_{eff}≡L)
or D delayed coordinates in the case of a single scalar observation
In the latter case we must introduce the delay parameter r and use the reduced data length L_{eff}≡L−(D−1)π, while in the former case L_{eff}≡L.
In the language of dynamical systems theory the data matrix is a trajectory of the system in the Ddimensional state space.
In a semiautonomous or fully autonomous mode of operation this step can be used to estimate parameters π, D and P (if required) automatically. If the origin of the signal or performance improvement goals do not dictate a particular preference, these default values can be used: π corresponds to a first minimum or a first zero (whichever is less) of the autocorrelation function of the signal, and D=3, P=2. Also important is the signaltonoise ratio (SNR) defined as:
and is measured in decibels (dB).
Estimation of generalized derivative (FIG. 1, block 103). The primary difference between our invention and a variety of devices based on regression schemes, as well as linear modeling techniques (ARMA models), is that we determine a relationship between the data and its rate of evolution expressed by the signal derivative. This provides us with a dynamical modeling framework. We further generalize this and propose that robust results can be obtained even for scalar signals generated by a multidimensional system, and for signals which were nonlinearly transformed on their way from the generator to the detector. Therefore, one can estimate the derivative (rate of signal evolution) in many ways depending upon desired output and signal properties. Here are several alternative algorithms which can be optionally used:
C1 leastsquares quadratic algorithm with smoothing; this estimator has proved to be extremely robust for very noisy signals (even with less than −20 dB signaltonoise ratios) and for nonstationary signals; the formula is:
where (2d+1) is the number of points taken for the estimation, Δt is the time (or length, if the derivative is spatial) between samples;
C2 higherorder estimators (for example, cubic algorithms) and algorithms minimizing products different from quadratic (for example, a sum of absolute values of differences between the signal derivative and its estimate); most of these techniques are less robust than C1 in the presence of significant noise, however, they can be used if a particular application suggests it;
C3 estimation from a singular value decomposition of the data matrix X (Broomhead, D. S.; King, G. P. Physica D, 20D (23), p.217 [2]); this yields a globallysmoothed derivative, which is suitable for stationary signals corrupted by weak noise (>10 dB);
C4 simple difference (“right” and “left”, correspondingly):
this estimator is sensitive to even small amounts of noise; such sensitivity can be useful for detecting weak noise in a smooth background of lowdimensional deterministic signal.
For any given processing chain we assume one algorithm should be used to generate output. This does not restrict a potential designer from using different algorithms in parallel, and to implement conditional probabilities and complex decision making schemes in a postprocessing unit (see below). In the following description we will refer to derivative scheme C1. As a result of this step a derivative vector B (in the case of scalar measurements) or a set of derivative vectors {B_{1}, B_{2}, . . . , BD} (in the case of multivariate measurements) is estimated. The length L_{w }of the derivative vector is the same as the number of points in the data sample, reduced by the number of points needed to estimate the derivative at the first and the last points.
Composing a design matrix (FIG. 1, block 104). In general, the design matrix (see, for example, “Numerical Recipes in C” by W. H. Press et. al., Cambridge University Press, 1992, page 671) has its column elements which are algebraically constructed from data values. Different rows represent different instances of observation (signal measurements). The rule used to compose column elements is called expansion. By changing expansions one can control the type of model for data analysis. There is an infinite number of ways a particular expansion can be composed, but there are only a few general types:
D1 Simple polynomial expansion (Taylor expansion). This is the most general local representation of the flow. We recommend it as a model for unknown signals or in the cases when many signals of different origin are processed.
D2 Rational polynomial expansion, being a ratio of two polynomial expansions.
D3 Expansion using a set of specific known orthogonal functions. This can be used if there is reason to believe that signalsofinterest may have a certain state space topology or spectral properties.
D4 Expansion in a set of empirical orthogonal functions obtained from a data matrix by orthogonalization (like singular value decomposition, or a GramSchmidt procedure). This expansion has features unique to a particular observed class of signals.
D5 Any of the above mentioned expansions, additionally including terms containing the independent variables (for example, time or coordinate). This type of expansion can be used to process nonstationary signals.
The same notion as for derivative calculation applies here: for any given processing chain the same expansion should be used to compare all outputs. In the following description we will refer to Expansion D1 except where indicated. The polynomial expansion (for delayed variables we put: x_{1}(i)≡(i+(D−1) ), . . . , x_{D}(i)≡x(i))
is characterized by order P and dimension D, and has N=(D+P)!/(D!P!) terms. The unknown coefficients {a_{0}, a_{1}, . . . , a_{N}} in the expansion are classification features which must be estimated as follows: first the N×L_{w }design matrix F is composed (L_{w}>N). For example, in the case of 2dimensional (D=2) second order (P=2) polynomial expansions:
For a particular application, choosing a particular model is still an art. Preferably, selection is based on physical properties of the system(s) generating the signal(s) under consideration. For example, to analyze short acoustic pulses we can choose a nonstationary quasilinear waveform described by the equations:
where a_{1 }and a_{2 }are proportional to the inverse width of the pulse and a_{3 }indicates characteristic frequency.
Also, note that to compose the design matrix we do not need the results from the previous step.
Estimation of classification features (FIG. 1, block 105). Our classification features, being the coefficients in the model expansion, must be estimated from the equations connecting the derivative (rate of evolution) with the design matrix. There are three general types of relations for the case of one independent variable. We describe each case separately, since implementations and output depend on the model type used. Nevertheless, all approaches address the same task—to estimate a vector of features A={a_{0}, a_{1}, . . . } which provides the best fit to the derivative B by the product F·A.
E1 Model based on a system of D coupled ordinary differential equations for vector observations. In this case we have an explicit model consisting of D equations for Ddimensional measurements: B_{k}=F·A_{k}, k=1, . . . , D. Correspondingly, we estimate the derivative for each component of the vector observation and solve D equations for D Ndimensional feature vectors A_{k}.
E2 Model based on a delayed differential equation. This is the most common case when the observation is a scalar variable, and the data matrix X and the design matrix F are composed from the delayed coordinates as explained by (7). In this case the rows of the matrix equation B=F·A become a differential equation with (D−1) delays taken at instants i=1, . . . , L_{w}. This is the most computationally efficient scheme, at least for cases when an analytic solution cannot be derived.
E3 Model based on an integral equation. This case is similar to E2, but before actual feature estimation, left and right sides of the equation B=FA are integrated (summed in the discrete case) over intervals l=1, . . . , L_{w}.
All preliminary steps are now complete at this point, and we can estimate features by solving approximately an overdetermined system of linear equations B=F·A (N variables, L_{w }equations). In the most general design, this can be done using a singular value decomposition (for example, see the algorithm in the book “Numerical Recipes in C” by W. H. Press et. al., Cambridge University Press, 1992, page 65). The solution can be expressed as A=V·diag(1/w_{j})·UT·B, where F=U·diag(w_{j})·V^{T }is the decomposition of the design matrix F into an L_{w}×N columnorthonormal matrix U, N×N diagonal matrix diag(w_{j}) with j=1, . . . , N positive or zero elements (singular values), and the transpose of an N×N orthogonal matrix V. Such a decomposition is known to provide a very robust solution of the leastsquare problem for overdetermined systems of linear equations. Potential singularities in the matrix equations can be eliminated by setting corresponding singular values to zero. Also, note that there are many other possibilities for solving an overdetermined system of linear equations in a leastsquare sense, or by minimizing some other difference functionals. Therefore, the solution by singular value decomposition should not be construed as a limitation on the scope of this invention. For example, for Cases D3 and D4 (when orthogonal expansions are used) the solution is provided simply by the following product:
where the row elements (j=1, . . . , N) of the design matrix F are in this case the basic orthogonal functions φ_{j}(x_{1}, x_{2}, . . . , x_{D}) divided by the normalization factor Ε_{i=1} ^{L} ^{ w }φ_{j} ^{2}(x_{1}(i), . . . , x_{D}(i). The latter approach may be preferable in a realtime operation where estimation of features must be provided rapidly. As a result of this step, a sample of data consisting of L_{w }vector or scalar measurements is mapped into N<<L_{w }features, which are coefficients of the equations describing the dynamics of the data. If we slide the observation window along the data, we obtain an ensemble of feature vectors A_{i}, i=1, . . . , N_{w}, where N_{w}, is the number of windows. In the ideal case of a long (L_{w}→∞) noisefree observation and a valid model, the distributions asymptotically approach deltafunctions. Correspondingly, short data samples, nonstationarity, noise, and suboptimal expansions will spread out the distributions, which then contain information about these effects.
Feature analysis and postprocessing (FIG. 1, block 106). Starting from this step in algorithm there are a variety of ways to utilize the estimated feature distributions, depending on the particular task or application. Because our device is based on a very novel use of the general theory of spatiotemporal evolution of dynamical systems, we cannot possibly foresee all applications and benefits of our invention. Here, we mention a few implementations which were designed by us during testing on simulated and realworld data. More specific implementations are also given below, where we describe how several embodiments of our invention operate. The postprocessing of feature distributions can be performed:
F1 by a human operator observing feature distributions on a computer display, printer, or any other device capable of visualizing the feature distributions or their numerical values;
F2 by using statistical estimators summarizing properties of the feature distributions such as statistical distance and Mean Discrimination Statistic (MDS) and its fractional moments (see Section “Operation of Invention” below, where General Purpose Classifier is described);
F3 by using classifiers such known in the art as that based on Mahalanobis distances (for example, Ray, S., and Turner, L. F. Information Sciences 60, p.217) [4], Samrnon's mapping (for example, Dzwinel, W. Pattern Recognition 27(7), p.949 [5]), neural nets (for example, Streit, R. L.; Luginbuhl T. E. IEEE Transactions on Neural Networks 5(5), 1994, p.764. [3]) and so on;
F4 by building threshold detectors in feature space based on standard signal processing schemes, for example, the NeymanPearson criterion;
F5 by comparing theoreticallyderived feature distributions (for example, for normally distributed noise) with those estimated from data;
F6 by utilizing distributions of features previously estimated from ground truth data, and stored in a database.
In almost all cases several statistical parameters are very useful for characterization of the feature distributions {A_{i}{a_{1}, a_{2}, . . . , a_{N}}_{i}i=1 . . . N_{w}}. They are:
1. weighted means (centers of distributions):
where Εγ_{k}=n_{w }are weights which can suppress outliers;
2. variances (standard deviations, spread of the distributions):
3. significance: S_{j}=a_{j}/σ_{aj};
4. histograms (discrete estimate of the probability density functions): H(a_{j}).
Histograms are the most widely used density estimator. The discontinuity of histograms can cause extreme difficulty if derivatives of the estimates are required. Therefore, in most applications a better estimator (such as kernel density estimators) should be chosen.
Though the above provides some options for data analyses and algorithm design, several preferred embodiments of the invention will be specifically addressed below through the description of their operation.
Generally, the operational mode of the algorithm depends on the embodiment of the invention. In any particular implementation, several control parameters in addition to those described above can be introduced. Given unconstrained time and computational power, many processing schemes can be incorporated into a single device. However, it is usually not necessary to provide such universality, since almost all advantages of the scheme can be gained based on a specialized embodiment of our invention best suited for a specific application. Therefore, we include below a description of several typical processing devices specifically optimized for preferred tasks.
General Purpose Detector of Deterministic Signals
From a theoretical study of dynamical modeling, we have derived the properties of feature distributions when the input signal X consists of Gaussian noise or sinusoidal waves. These distributions can be obtained with arbitrary accuracy analytically and also numerically on simulated data sets.
The purpose of this embodiment is to estimate a probability for the given observation to belong to either a purely random process, or to a process different from this, i.e. containing a deterministic component. We will assume that a probability density functions P_{0}(a_{j}, L_{w}), being the PDF, for the specified class of signals was preliminarily calculated using theoretical reasoning; or estimated from long simulated (or measured) data sets normalized to zero mean and unit variance. Normalization is not mandatory, but is a convenient way to exclude amplitude variations. As we indicated, the PDF depends on window length, thus a scaling relation should also be derived.
The detector is built using the following components:
1. Preprocessor normalizing data to zero mean and unit variance (FIG. 2, block 201).
2. Derivative estimated using leastsquares quadratic algorithm (FIG. 2, block 202).
3. Polynomial expansion (D=2, P=2) used in conjunction with the model based on delayed differential equation (FIG. 2, block 203).
4. The system of linear algebraic equations is solved using SVD algorithm as described above (FIG. 2, block 204).
5. Estimated probabilities are calculated using model probability densities P(a_{j}), where a_{j}, j=1, . . . , 6 are estimated coefficients (FIG. 2, block 205).
One modification of this device can greatly improve performance statistics. If many observations (windows) of the signalofinterest are available prior to the actual detection task, we can approximate the PDF from its feature distributions P_{1}(a_{j}, L_{w}) using functional fits. There are several ways to estimate PDF from discrete sets known in the prior art. For example, kernel density estimation is described in details in the book “Density Estimation For Statistical and Data Analysis” by B. W. Silverman [6]. The details are beyond the scope of this invention.
Once the P_{1}(a_{j}, L_{w}) is estimated one can use a NeymanPearson criterion and build a threshold detector (for example, see in the book “detection of Signals in Noise” by R. McDonough and A. Whalen, Academic Press, 1995 [7]). Using a “Probability of Detection”, “Probability of False Alarm” framework (P_{d}, P_{fa}), the desired P_{fa }is chosen and the threshold a_{th }is estimated from the following relation (the integral should be substituted by sum in the discrete case):
Now, if a new sample is observed with a <ath and we know that it belongs either to P_{0}(a) (“noise”) or P_{1}(a) (“signal”), then the probability of detection of the signal is
while P_{fa }is not higher than chosen.
Multivariate threshold detectors can also be built using several features by implementing either a joint probability framework or simply numerically estimating P_{d }by counting events of correct and false detection, during preliminary training in a controlled experimental environment.
General Purpose Classifier
Suppose that the task is to classify N_{s }signals from N_{c }known distinct classes. We assume that coefficients {a_{j}j=1 . . . N} are estimated using one of the previously described algorithms. Here, we describe the postprocessing unit for general classification.
It is convenient to define several classification measures in feature space. The Euclidean distance between two feature distribution centers a_{k} ^{(1)} and a_{k} ^{(2)} is:
This distance cannot be directly used as a measure of separation between distributions, because it does not include statistical information about how strongly the distributions overlap. Instead, we define the Statistical Distance as a normalized, dimensionless distance between feature distributions ({a_{k} ^{(1)}} and {a_{k} ^{(}2)}):
where σ_{Ε} is a projected total standard deviation:
Obviously, R_{ij}=R_{ji}, so we have only N_{c}(N_{c}−1)/2 different numbers. This statistical distance now expresses the distribution separation in terms of mean standard deviations. Further, we define the Mean Discrimination Statistic (MDS) to be the arithmetic average of all pairwise statistical distances between all distributions:
where N_{c }is the number of distributions (possible classes). It is also useful to define fractional “moments” of the MDS:
If all class distributions are equally separated in a particular feature space, then MDS(1)=MDS(1/2). In an opposite case, when all but one class distribution form a dense cluster, while one class distribution is very remote, then MDS(1)>MDS(1/2). Hence, the MDS can be used as a design criteria in choosing the classifier model parameters.
Note that pairwise classification is based on R_{12 }distances, which can be translated into detection probabilities according to the statistical scheme appropriate for a particular application. For example, it can be the NeymanPearson criterion described above.
It is convenient to format decision output as a table of pairwise statistical distances R_{ij}. For example, for 3 signals:
signal 1  signal 2  signal 3  
signal 1  0  R_{12}  R_{13}  
signal 2  R_{12}  0  R_{23}  
signal 3  R_{13}  R_{23}  0  
If the number R_{ij }is greater than a certain threshold (we often use 1 as a criterion of good separation), then distributions of features from signals i and j can be considered to be well discriminated.
6.3 TimeEvolving Image Classifier
One important potential application of our invention can be the classification of evolving patterns (images) generated by a spatiotemporal dynamical system. This embodiment describes how to modify the processing chain to include 2D image processing (for higher dimensional “images” the generalization is straightforward). This modification basically involves the design matrix only, namely the way the expansion in spatial indices is constructed. We assume that the data in this embodiment is represented by a 2D matrix evolving in time: {X(t)i=1 . . . L_{1}, j=1 . . . L_{2}}. Correspondingly we have a twocomponent derivative which we will call B_{1 }and B_{2}. The geometry of the 2D image under consideration is not necessarily Euclidean, thus “1” and “2” are not necessarily “x” and “y”, but can be “distance” and “angle” in polar coordinates, for example. We will assume in the following description that the image has Euclidean geometry, but this should not restrict application to patterns measured in different coordinate systems.
In general, the expansion can include more than the nearestneighbor points, and can even be nonlocal, but we will consider it here to be local and to include a single delay parameter for the sake of clarity:
This is a much longer expansion than the simple polynomial one for scalar signals (see D1 definition in the description of the general processing chain). For P=2 and a single delay it includes 28 monomials. Also, note that such expansion takes into account spatial derivatives up to the second order only.
Two equations result from the projections on x and y directions:
Therefore, the total number of features is 2×28=56. We recommend to reduce the number of termsusing symmetry considerations and physical reasoning appropriate for a given application. Our experience shows that most of the features will be nonsignificant, since an expansion like Eq. (19) is too general.
Postprocessing of features and subsequent classification can be performed using any of the previously described techniques, but not restricted to them.
6.4 Nonstationary Signal Transformer
This embodiment of the invention simply maps a signal (L_{w }numbers) into feature vectors (N numbers) using any of the specified above designs. Due to the fact that usually L_{w}<<N an enormous compression is achieved. If a sensor(s) is remotely located from the postprocessing unit it allows for very economic data transfer. The window L_{w }slides along the data with a window shift L_{s }allowing for overlapping windows (if L_{s}<L_{w}).
This embodiment can also be considered as a functional part of any device built as embodiment of our invention. It simply incorporates blocks 102, 103, 104 and 105 shown in FIG. 1. For example, in the “General Purpose Detector of Deterministic Signals” (first embodiment) the blocks performing transformation are 201, 202, 203 and 204 grouped in FIG. 2.
Obviously, the transformation is applicable to nonstationary signals as well. This embodiment of the invention addresses possible changes in the signal under consideration reflected by the evolution of the features. By following feature trajectories in a feature space, one can study the changes in the signal pattern using highly compressed information about its dynamics. There are several potential applications of this embodiment, including: speech and voice recognition, biosonar characterization, motion detectors and so on.
[1] Gouesbet, G., and Letellier, C. “Global VectorField Reconstruction By Using a Multivariate Polynomial L2 Approximation on Nets”. Physical Review E 49(6), p.4955 (1994).
[2] Broomhead, D. S.; King, G. P. “Extracting Qualitative Dynamics From Experimental Data”. Physica D, 20D(23), p.217 (1986).
[3] Streit, R. L.; Luginbuhl T. E. “Maximum Likelihood Training of Probabilistic Neural Networks”. IEEE Transactions on Neural Networks 5(5), p.764 (1994).
[4] Ray, S.; Turner, L. F. “Mahalanobis DistanceBased Two New Feature Evaluation Criteria”. Information Sciences 60, p.217 (1992).
[5] Dzwinel, W. “How To Make Sammon's Mapping Useful For Multidimensional Data Structures Analysis”. Pattern Recognition 27(7), p.949.
[6] Silverman, B. W. “Density Estimate For Statistical and Data Analysis”. Chapman and Hall, London—N.Y., 1986.
[7] McDonough, R., and Whalen, A. “Detection of Signals in Noise”. Academic Press, 1995.
[8] Crutchfield, J. P.; McNamara, B. S. “Equations of motion from a data series”. Complex Systems 1(3), p.41752 (1987).
Claims (11)
Priority Applications (3)
Application Number  Priority Date  Filing Date  Title 

US5157997 true  19970702  19970702  
US09105529 US6278961B1 (en)  19970702  19980626  Signal and pattern detection or classification by estimation of continuous dynamical models 
US09932450 US6564176B2 (en)  19970702  20010817  Signal and pattern detection or classification by estimation of continuous dynamical models 
Applications Claiming Priority (1)
Application Number  Priority Date  Filing Date  Title 

US09932450 US6564176B2 (en)  19970702  20010817  Signal and pattern detection or classification by estimation of continuous dynamical models 
Related Parent Applications (1)
Application Number  Title  Priority Date  Filing Date  

US09105529 Continuation US6278961B1 (en)  19970702  19980626  Signal and pattern detection or classification by estimation of continuous dynamical models 
Publications (2)
Publication Number  Publication Date 

US20020133317A1 true US20020133317A1 (en)  20020919 
US6564176B2 true US6564176B2 (en)  20030513 
Family
ID=26729583
Family Applications (2)
Application Number  Title  Priority Date  Filing Date 

US09105529 Active  Reinstated US6278961B1 (en)  19970702  19980626  Signal and pattern detection or classification by estimation of continuous dynamical models 
US09932450 Active US6564176B2 (en)  19970702  20010817  Signal and pattern detection or classification by estimation of continuous dynamical models 
Family Applications Before (1)
Application Number  Title  Priority Date  Filing Date 

US09105529 Active  Reinstated US6278961B1 (en)  19970702  19980626  Signal and pattern detection or classification by estimation of continuous dynamical models 
Country Status (1)
Country  Link 

US (2)  US6278961B1 (en) 
Cited By (25)
Publication number  Priority date  Publication date  Assignee  Title 

US20030009470A1 (en) *  20010425  20030109  Leary James F.  Subtractive clustering for use in analysis of data 
US6732064B1 (en) *  19970702  20040504  Nonlinear Solutions, Inc.  Detection and classification system for analyzing deterministic properties of data using correlation parameters 
US6823294B1 (en) *  19990720  20041123  Collett International, Inc.  Method and system for measuring circuit design capability 
US20040243328A1 (en) *  20030528  20041202  Rapp Paul Ernest  System and method for categorical analysis of time dependent dynamic processes 
US20050025355A1 (en) *  20030731  20050203  Simard Patrice Y.  Elastic distortions for automatic generation of labeled data 
US20050163374A1 (en) *  20040128  20050728  Ferman A. M.  Methods and systems for automatic detection of continuoustone regions in document images 
US20050177349A1 (en) *  20040205  20050811  Honeywell International Inc.  Apparatus and method for isolating noise effects in a signal 
US20050177348A1 (en) *  20040205  20050811  Honeywell International Inc.  Apparatus and method for modeling relationships between signals 
US20060056703A1 (en) *  20040913  20060316  Scimed Life Systems, Inc.  Systems and methods for producing a dynamic classified image 
US20060084881A1 (en) *  20041020  20060420  Lev Korzinov  Monitoring physiological activity using partial state space reconstruction 
US20060098845A1 (en) *  20041105  20060511  Kyprianos Papademetriou  Digital signal processing methods, systems and computer program products that identify threshold positions and values 
US20060135217A1 (en) *  20041220  20060622  Sung ChiaChi  Alerting method for recharging mobile devices 
US7085688B1 (en) *  19991022  20060801  Shizuo Sumida  Nonlinear characteristic reproducing apparatus and nonlinear characteristic reproducing program storage medium 
US20060222221A1 (en) *  20050405  20061005  Scimed Life Systems, Inc.  Systems and methods for image segmentation with a multistage classifier 
US20070211834A1 (en) *  20060313  20070913  Lockheed Martin Corporation  Emitter pulse detection utilizing adaptive matched filter approach 
US20070219453A1 (en) *  20060314  20070920  Michael Kremliovsky  Automated analysis of a cardiac signal based on dynamical characteristics of the cardiac signal 
US20090081722A1 (en) *  20020912  20090326  Invitrogen Corporation  Sitespecific labeling of affinity tags in fusion proteins 
US20090299496A1 (en) *  20060713  20091203  Bae Systems  Controller 
US7720013B1 (en) *  20041012  20100518  Lockheed Martin Corporation  Method and system for classifying digital traffic 
US20100204599A1 (en) *  20090210  20100812  Cardionet, Inc.  Locating fiducial points in a physiological signal 
US20110034142A1 (en) *  20071108  20110210  James Roland Jordan  Detection of transient signals in doppler spectra 
US20110307155A1 (en) *  20090224  20111215  Simard Christian  Method and system for limiting a dynamic parameter of a vehicle 
US20130201050A1 (en) *  20100217  20130808  Saab Ab  Wideband transmitter/receiver arrangement for multifunctional radar and communication 
US20130279804A1 (en) *  20120423  20131024  Daniel Kilbank  Dual transform lossy and lossless compression 
US20130286041A1 (en) *  20120430  20131031  Conocophillips Company  Multidimensional data reconstruction constrained by a regularly interpolated model 
Families Citing this family (23)
Publication number  Priority date  Publication date  Assignee  Title 

US6278961B1 (en) *  19970702  20010821  Nonlinear Solutions, Inc.  Signal and pattern detection or classification by estimation of continuous dynamical models 
US6401057B1 (en) *  19970702  20020604  Nonlinear Solutions, Inc.  Detection and classification system for analyzing deterministic properties of data using correlation parameters 
JP4517409B2 (en) *  19981109  20100804  ソニー株式会社  Data processing apparatus and data processing method 
JP4147647B2 (en) *  19981109  20080910  ソニー株式会社  Data processing apparatus and data processing method, and recording medium 
JP4164712B2 (en) *  19990209  20081015  ソニー株式会社  Data processing apparatus and data processing method 
JP4344964B2 (en) *  19990601  20091014  ソニー株式会社  Image processing apparatus and image processing method 
US6832052B1 (en) *  20000516  20041214  Eci Telecom Ltd.  Optical transponder 
WO2002031815A1 (en) *  20001013  20020418  Science Applications International Corporation  System and method for linear prediction 
US6594622B2 (en) *  20001129  20030715  International Business Machines Corporation  System and method for extracting symbols from numeric time series for forecasting extreme events 
US20020183984A1 (en) *  20010605  20021205  Yining Deng  Modular intelligent multimedia analysis system 
JP3891807B2 (en)  20010914  20070314  ジーイー・メディカル・システムズ・グローバル・テクノロジー・カンパニー・エルエルシー  Failure prediction apparatus and method of the superconducting magnet and a magnetic resonance imaging system, 
US7016805B2 (en) *  20011214  20060321  Wavecrest Corporation  Method and apparatus for analyzing a distribution 
US8082286B1 (en)  20020422  20111220  Science Applications International Corporation  Method and system for softweighting a reiterative adaptive signal processor 
US7415065B2 (en) *  20021025  20080819  Science Applications International Corporation  Adaptive filtering in the presence of multipath 
WO2005048018A3 (en) *  20031023  20051103  Clearsight Systems Inc  Method for optimization using state functions 
US7287012B2 (en) *  20040109  20071023  Microsoft Corporation  Machinelearned approach to determining document relevance for search over large electronic collections of documents 
FR2874768B1 (en) *  20040831  20061215  Ecole Polytechnique Etablissem  signal compressing method 
US20110199861A1 (en) *  20070312  20110818  Elta Systems Ltd.  Method and system for detecting motorized objects 
EP2131594B1 (en) *  20080606  20130814  MaxPlanckGesellschaft zur Förderung der Wissenschaften e.V.  Method and device for image compression 
FR2945340B1 (en) *  20090511  20111125  Commissariat Energie Atomique  Method for Characterization of tactile surface texture. 
US20120053895A1 (en) *  20100818  20120301  Noam Amir  Method and system for evaluating the condition of a collection of similar elongated hollow objects 
CN104216015B (en) *  20140901  20170412  电子科技大学  Based on 3D seismic signal classification Latent Dirichlet Allocation of 
CN105929380A (en) *  20151201  20160907  中国科学院上海技术物理研究所  Fullwaveform laser radar data denoising method for satellite laser altimeter 
Citations (9)
Publication number  Priority date  Publication date  Assignee  Title 

US6181976B2 (en) *  
US5453940A (en) *  19910322  19950926  The Secretary Of The State For Defence In Her Britannic Majesty's Government Of The United Kingdom Of Great Britain And Northern Ireland  Dynamical system analyser 
US5617513A (en) *  19920306  19970401  Schnitta; Bonnie S.  Method for analyzing activity in a signal 
US6131089A (en) *  19980504  20001010  Motorola, Inc.  Pattern classifier with training system and methods of operation therefor 
US6181976B1 (en) *  19970418  20010130  Larry Stephen Chandler  Adept data processor implementing function similation with inverse deviation variation weighting 
US6192353B1 (en) *  19980209  20010220  Motorola, Inc.  Multiresolutional classifier with training system and method 
US6240282B1 (en) *  19980713  20010529  Motorola, Inc.  Apparatus for performing nonlinear signal classification in a communications system 
US6278961B1 (en) *  19970702  20010821  Nonlinear Solutions, Inc.  Signal and pattern detection or classification by estimation of continuous dynamical models 
US6349272B1 (en) *  19990407  20020219  Cadence Design Systems, Inc.  Method and system for modeling timevarying systems and nonlinear systems 
Patent Citations (11)
Publication number  Priority date  Publication date  Assignee  Title 

US6181976B2 (en) *  
US5453940A (en) *  19910322  19950926  The Secretary Of The State For Defence In Her Britannic Majesty's Government Of The United Kingdom Of Great Britain And Northern Ireland  Dynamical system analyser 
US5493516A (en) *  19910322  19960220  The Secretary Of State For Defence In Her Britannic Majesty's Government Of The United Kingdom Of Great Britain And Northern Ireland  Dynamical system analyzer 
US5835682A (en) *  19910322  19981110  The Secretary Of State For Defence In Her Britannic Majesty's Government Of The United Kingdom Of Great Britain And Northern Ireland  Dynamical system analyzer 
US5617513A (en) *  19920306  19970401  Schnitta; Bonnie S.  Method for analyzing activity in a signal 
US6181976B1 (en) *  19970418  20010130  Larry Stephen Chandler  Adept data processor implementing function similation with inverse deviation variation weighting 
US6278961B1 (en) *  19970702  20010821  Nonlinear Solutions, Inc.  Signal and pattern detection or classification by estimation of continuous dynamical models 
US6192353B1 (en) *  19980209  20010220  Motorola, Inc.  Multiresolutional classifier with training system and method 
US6131089A (en) *  19980504  20001010  Motorola, Inc.  Pattern classifier with training system and methods of operation therefor 
US6240282B1 (en) *  19980713  20010529  Motorola, Inc.  Apparatus for performing nonlinear signal classification in a communications system 
US6349272B1 (en) *  19990407  20020219  Cadence Design Systems, Inc.  Method and system for modeling timevarying systems and nonlinear systems 
NonPatent Citations (2)
Title 

Moreno et al.,"A Vector Taylor Series Approach for EnvironmentIndependent Speech Recognition", IEEE, copyright 1995.* * 
Scott et al., "Nonlinear System Identification and Prediction Using Orthonormal Functions", IEEE, Jul. 1997. * 
Cited By (45)
Publication number  Priority date  Publication date  Assignee  Title 

US6732064B1 (en) *  19970702  20040504  Nonlinear Solutions, Inc.  Detection and classification system for analyzing deterministic properties of data using correlation parameters 
US6823294B1 (en) *  19990720  20041123  Collett International, Inc.  Method and system for measuring circuit design capability 
US7085688B1 (en) *  19991022  20060801  Shizuo Sumida  Nonlinear characteristic reproducing apparatus and nonlinear characteristic reproducing program storage medium 
US7043500B2 (en) *  20010425  20060509  Board Of Regents, The University Of Texas Syxtem  Subtractive clustering for use in analysis of data 
US20030009470A1 (en) *  20010425  20030109  Leary James F.  Subtractive clustering for use in analysis of data 
US9164099B2 (en)  20020912  20151020  Life Technologies Corporation  Sitespecific labeling of affinity tags in fusion proteins 
US20090081722A1 (en) *  20020912  20090326  Invitrogen Corporation  Sitespecific labeling of affinity tags in fusion proteins 
US7117108B2 (en) *  20030528  20061003  Paul Ernest Rapp  System and method for categorical analysis of time dependent dynamic processes 
US20040243328A1 (en) *  20030528  20041202  Rapp Paul Ernest  System and method for categorical analysis of time dependent dynamic processes 
US20050025355A1 (en) *  20030731  20050203  Simard Patrice Y.  Elastic distortions for automatic generation of labeled data 
US7418128B2 (en) *  20030731  20080826  Microsoft Corporation  Elastic distortions for automatic generation of labeled data 
US20050163374A1 (en) *  20040128  20050728  Ferman A. M.  Methods and systems for automatic detection of continuoustone regions in document images 
US7379594B2 (en) *  20040128  20080527  Sharp Laboratories Of America, Inc.  Methods and systems for automatic detection of continuoustone regions in document images 
US7574333B2 (en) *  20040205  20090811  Honeywell International Inc.  Apparatus and method for modeling relationships between signals 
US20050177348A1 (en) *  20040205  20050811  Honeywell International Inc.  Apparatus and method for modeling relationships between signals 
US20050177349A1 (en) *  20040205  20050811  Honeywell International Inc.  Apparatus and method for isolating noise effects in a signal 
US7363200B2 (en)  20040205  20080422  Honeywell International Inc.  Apparatus and method for isolating noise effects in a signal 
US20060056703A1 (en) *  20040913  20060316  Scimed Life Systems, Inc.  Systems and methods for producing a dynamic classified image 
US7460716B2 (en)  20040913  20081202  Boston Scientific Scimed, Inc.  Systems and methods for producing a dynamic classified image 
US7720013B1 (en) *  20041012  20100518  Lockheed Martin Corporation  Method and system for classifying digital traffic 
US7996075B2 (en)  20041020  20110809  Cardionet, Inc.  Monitoring physiological activity using partial state space reconstruction 
US20060084881A1 (en) *  20041020  20060420  Lev Korzinov  Monitoring physiological activity using partial state space reconstruction 
US20060098845A1 (en) *  20041105  20060511  Kyprianos Papademetriou  Digital signal processing methods, systems and computer program products that identify threshold positions and values 
US7583819B2 (en)  20041105  20090901  Kyprianos Papademetriou  Digital signal processing methods, systems and computer program products that identify threshold positions and values 
US20060135217A1 (en) *  20041220  20060622  Sung ChiaChi  Alerting method for recharging mobile devices 
US7680307B2 (en)  20050405  20100316  Scimed Life Systems, Inc.  Systems and methods for image segmentation with a multistage classifier 
US20060222221A1 (en) *  20050405  20061005  Scimed Life Systems, Inc.  Systems and methods for image segmentation with a multistage classifier 
US8175368B2 (en)  20050405  20120508  Scimed Life Systems, Inc.  Systems and methods for image segmentation with a multistate classifier 
US20110211745A1 (en) *  20050405  20110901  Scimed Life Systems, Inc.  Systems and methods for image segmentation with a multistage classifier 
US7929647B2 (en)  20060313  20110419  Lockheed Martin Corporation  Emitter pulse detection utilizing adaptive matched filter approach 
US20070211834A1 (en) *  20060313  20070913  Lockheed Martin Corporation  Emitter pulse detection utilizing adaptive matched filter approach 
US20070219453A1 (en) *  20060314  20070920  Michael Kremliovsky  Automated analysis of a cardiac signal based on dynamical characteristics of the cardiac signal 
US7729753B2 (en)  20060314  20100601  Cardionet, Inc.  Automated analysis of a cardiac signal based on dynamical characteristics of the cardiac signal 
US20090299496A1 (en) *  20060713  20091203  Bae Systems  Controller 
US7966276B2 (en) *  20060713  20110621  Bae Systems  Controller for partially observable systems 
US8022864B2 (en)  20071108  20110920  The United States Of America As Represented By The Secretary Of Commerce  Detection of transient signals in doppler spectra 
US20110034142A1 (en) *  20071108  20110210  James Roland Jordan  Detection of transient signals in doppler spectra 
US8200319B2 (en)  20090210  20120612  Cardionet, Inc.  Locating fiducial points in a physiological signal 
US20100204599A1 (en) *  20090210  20100812  Cardionet, Inc.  Locating fiducial points in a physiological signal 
US20110307155A1 (en) *  20090224  20111215  Simard Christian  Method and system for limiting a dynamic parameter of a vehicle 
US9071337B2 (en) *  20100217  20150630  Saab Ab  Wideband transmitter/receiver arrangement for multifunctional radar and communication 
US20130201050A1 (en) *  20100217  20130808  Saab Ab  Wideband transmitter/receiver arrangement for multifunctional radar and communication 
US20130279804A1 (en) *  20120423  20131024  Daniel Kilbank  Dual transform lossy and lossless compression 
US20130286041A1 (en) *  20120430  20131031  Conocophillips Company  Multidimensional data reconstruction constrained by a regularly interpolated model 
US9158016B2 (en) *  20120430  20151013  Conocophillips Company  Multidimensional data reconstruction constrained by a regularly interpolated model 
Also Published As
Publication number  Publication date  Type 

US20020133317A1 (en)  20020919  application 
US6278961B1 (en)  20010821  grant 
Similar Documents
Publication  Publication Date  Title 

Stoica et al.  Maximum likelihood estimation of the parameters of multiple sinusoids from noisy measurements  
Junninen et al.  Methods for imputation of missing values in air quality data sets  
Aminghafari et al.  Multivariate denoising using wavelets and principal component analysis  
Schweizer et al.  Efficient detection in hyperspectral imagery  
Pratt  Correlation techniques of image registration  
Van der Baan et al.  Neural networks in geophysical applications  
Plaza et al.  Spatial/spectral endmember extraction by multidimensional morphological operations  
Pace et al.  Sparse spatial autoregressions  
US6226321B1 (en)  Multichannel parametric adaptive matched filter receiver  
Frery et al.  A model for extremely heterogeneous clutter  
US5170440A (en)  Perceptual grouping by multiple hypothesis probabilistic data association  
Rellier et al.  Texture feature analysis using a GaussMarkov model in hyperspectral image classification  
Gibson et al.  An analytic approach to practical state space reconstruction  
Gini et al.  Covariance matrix estimation for CFAR detection in correlated heavy tailed clutter  
Wikle et al.  A dimensionreduced approach to spacetime Kalman filtering  
ColeRhodes et al.  Multiresolution registration of remote sensing imagery by optimization of mutual information using a stochastic gradient  
Gilholm et al.  Poisson models for extended target and group tracking  
Full et al.  FUZZY QMODEL—A new approach for linear unmixing  
US6430307B1 (en)  Feature extraction system and face image recognition system  
Fukunaga et al.  An algorithm for finding intrinsic dimensionality of data  
Hall et al.  A functional data—analytic approach to signal discrimination  
Pal et al.  An assessment of the effectiveness of decision tree methods for land cover classification  
Giannakis et al.  Signal detection and classification using matched filtering and higher order statistics  
Hwang et al.  Nonparametric multivariate density estimation: a comparative study  
Rao et al.  Subset selection in noise based on diversity measure minimization 
Legal Events
Date  Code  Title  Description 

AS  Assignment 
Owner name: KADTKE, JAMES, DISTRICT OF COLUMBIA Free format text: NUNC PRO TUNC ASSIGNMENT;ASSIGNOR:NONLINEAR SOLUTIONS, INC.;REEL/FRAME:018109/0514 Effective date: 20060807 

AS  Assignment 
Owner name: OMNITURE, INC., UTAH Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:KADTKE, JAMES;REEL/FRAME:018109/0942 Effective date: 20060210 

FPAY  Fee payment 
Year of fee payment: 4 

AS  Assignment 
Owner name: WELLS FARGO FOOTHILL, LLC, AS AGENT, CALIFORNIA Free format text: SECURITY AGREEMENT;ASSIGNOR:OMNITURE, INC.;REEL/FRAME:022078/0141 Effective date: 20081224 

AS  Assignment 
Owner name: OMNITURE, INC., UTAH Free format text: RELEASE BY SECURED PARTY;ASSIGNOR:WELLS FARGO FOOTHILL, LLC;REEL/FRAME:023525/0335 Effective date: 20091023 

AS  Assignment 
Owner name: ADOBE SYSTEMS INCORPORATED, CALIFORNIA Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:OMNITURE, INC.;REEL/FRAME:023538/0077 Effective date: 20091112 Owner name: ADOBE SYSTEMS INCORPORATED,CALIFORNIA Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:OMNITURE, INC.;REEL/FRAME:023538/0077 Effective date: 20091112 

FPAY  Fee payment 
Year of fee payment: 8 

FPAY  Fee payment 
Year of fee payment: 12 